Structure-based classification of EGFR mutations informs inhibitor selection for lung cancer therapy

Abstract

EGFR oncogenic mutations predict sensitivity to EGFR inhibitors in NSCLC, but less is known about EGFR "variants of unknown significance." Using preclinical models, 3D structure analyses, and patient response data, Robichaux et al. show in Nature that mutations in structural regions of EGFR predict responses to different EGFR inhibitors.

Figure 1.. Structure-based classification of EGFR mutations matches more patients to efficacious EGFR inhibitors than prior schemes

Left, cartoon lollipop diagram showing mutation location, frequency (height of lollipop; as identified by Robichaux et al., 2021) and mutation classes (colored circles) in the kinase domain of EGFR using the prior classification scheme (top) and the structure-based classification proposed by Robichaux et al. (bottom). Only mutations found at >1% frequency by Robichaux et al. are shown; T790M-like mutations are not displayed due to low frequency. Right, color key for circles in the lollipop diagram and predicted sensitivity of each mutation class to first-, second-, and third-generation EGFR inhibitors. Exon 20 (Ex20) loop insertions may be sensitive to second-generation or most EGFR inhibitors depending on their location (as discussed in the text) but are shown as only sensitive to second-generation inhibitors, for simplicity. First-generation EGFR inhibitors are erlotinib, gefitinib, AZD3759, and sapatinib; second-generation inhibitors are afatinib, dacomitinib, neratinib, and poziotinib; third-generation inhibitors are osimertinib, nazaratinib, olmutinib, rociletinib, naquotinib, and lazertinib.